U.S. patent application number 16/921713 was filed with the patent office on 2022-01-06 for optical character recognition (ocr) induction for multi-page changes.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Corville O. ALLEN, Andrew R. FREED, Joseph KOZHAYA, Shikhar KWATRA.
Application Number | 20220004755 16/921713 |
Document ID | / |
Family ID | 1000004987352 |
Filed Date | 2022-01-06 |
United States Patent
Application |
20220004755 |
Kind Code |
A1 |
ALLEN; Corville O. ; et
al. |
January 6, 2022 |
OPTICAL CHARACTER RECOGNITION (OCR) INDUCTION FOR MULTI-PAGE
CHANGES
Abstract
Provided are techniques for OCR induction for multi-page
changes. A plurality of documents of a document type are processed
to generate text area data for a text area in one or more documents
of the plurality of documents, where the text area data includes
coordinate locations of a zone for the text area based on expansion
and direction of shift of the text area. A page flow model is
trained using the plurality of documents and the text area data. In
response to receiving a new document comprising the text area, a
scanning script is received from the page flow model, where the
page flow model identifies a new zone for the text area in the new
document and determines how to adjust another zone for an element
in the new document. The scanning script is used to scan the new
document to generate digital text.
Inventors: |
ALLEN; Corville O.;
(Morrisville, NC) ; KOZHAYA; Joseph; (Morrisville,
NC) ; KWATRA; Shikhar; (Raleigh, NC) ; FREED;
Andrew R.; (Cary, NC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Family ID: |
1000004987352 |
Appl. No.: |
16/921713 |
Filed: |
July 6, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06V 10/98 20220101;
H04N 1/047 20130101; G06V 30/10 20220101; G06K 9/6256 20130101;
G06V 30/414 20220101 |
International
Class: |
G06K 9/00 20060101
G06K009/00; G06K 9/62 20060101 G06K009/62; G06K 9/03 20060101
G06K009/03; H04N 1/047 20060101 H04N001/047 |
Claims
1. A computer-implemented method, comprising operations for:
processing a plurality of documents of a document type to generate
text area data for a text area in one or more documents of the
plurality of documents, wherein the text area data includes
coordinate locations of a zone for the text area based on expansion
and direction of shift of the text area, and wherein the expansion
is based on a font type and a font size of text values in the text
area; training a page flow model using the plurality of documents
and the text area data; and in response to receiving a new document
comprising the text area, receiving a scanning script from the page
flow model, wherein the page flow model identifies a new zone for
the text area in the new document and determines how to adjust
another zone for an element in the new document, and wherein the
page flow model generates the scanning script to describe new
coordinate locations of the new zone and the another zone in the
new document; and using the scanning script to scan the new
document to generate digital text.
2. The computer-implemented method of claim 1, further comprising
operations for: generating a plurality of page flow models, wherein
each of the page flow models is associated with a different
document type, and wherein another document type of another
document is used to select a corresponding page flow model of the
plurality of page flow models.
3. The computer-implemented method of claim 1, wherein the page
flow model further comprises: extracting text values for the text
area in the new document; determining that the text values are
accurate; and producing intermediate output that includes the new
coordinate locations of the new zone for the text area, a label for
the text area, and text the text values for the text area.
4. The computer-implemented method of claim 3, wherein the page
flow model further comprises: calculating a percentage of
inaccuracy of the new zone and the another zone; and for the new
zone and the another zone, adjusting a size of that zone until the
percentage of inaccuracy for that zone is less than an inaccuracy
threshold.
5. The computer-implemented method of claim 3, wherein the scanning
script comprises, for the new zone, the adjusted coordinate
locations, the label, the text values, and an extraction
confidence.
6. The computer-implemented method of claim 1, wherein a Software
as a Service (SaaS) is configured to perform the operations of the
computer-implemented method.
7. A computer program product, the computer program product
comprising a computer readable storage medium having program code
embodied therewith, the program code executable by at least one
processor to perform operations for: processing a plurality of
documents of a document type to generate text area data for a text
area in one or more documents of the plurality of documents,
wherein the text area data includes coordinate locations of a zone
for the text area based on expansion and direction of shift of the
text area, and wherein the expansion is based on a font type and a
font size of text values in the text area; training a page flow
model using the plurality of documents and the text area data; and
in response to receiving a new document comprising the text area,
receiving a scanning script from the page flow model, wherein the
page flow model identifies a new zone for the text area in the new
document and determines how to adjust another zone for an element
in the new document, and wherein the page flow model generates the
scanning script to describe new coordinate locations of the new
zone and the another zone in the new document; and using the
scanning script to scan the new document to generate digital
text.
8. The computer program product of claim 7, wherein the program
code is executable by the at least one processor to perform further
operations for: generating a plurality of page flow models, wherein
each of the page flow models is associated with a different
document type, and wherein another document type of another
document is used to select a corresponding page flow model of the
plurality of page flow models.
9. The computer program product of claim 7, wherein the page flow
model further comprises: extracting text values for the text area
in the new document; determining that the text values are accurate;
and producing intermediate output that includes the new coordinate
locations of the new zone for the text area, a label for the text
area, and text the text values for the text area.
10. The computer program product of claim 9, wherein the page flow
model further comprises: calculating a percentage of inaccuracy of
the new zone and the another zone; and for the new zone and the
another zone, adjusting a size of that zone until the percentage of
inaccuracy for that zone is less than an inaccuracy threshold.
11. The computer program product of claim 9, wherein the scanning
script comprises, for the new zone, the adjusted coordinate
locations, the label, the text values, and an extraction
confidence.
12. The computer program product of claim 7, wherein a Software as
a Service (SaaS) is configured to perform the operations of the
computer program product.
13. A computer system, comprising: one or more processors, one or
more computer-readable memories and one or more computer-readable,
tangible storage devices; and program instructions, stored on at
least one of the one or more computer-readable, tangible storage
devices for execution by at least one of the one or more processors
via at least one of the one or more memories, to perform operations
comprising: processing a plurality of documents of a document type
to generate text area data for a text area in one or more documents
of the plurality of documents, wherein the text area data includes
coordinate locations of a zone for the text area based on expansion
and direction of shift of the text area, and wherein the expansion
is based on a font type and a font size of text values in the text
area; training a page flow model using the plurality of documents
and the text area data; and in response to receiving a new document
comprising the text area, receiving a scanning script from the page
flow model, wherein the page flow model identifies a new zone for
the text area in the new document and determines how to adjust
another zone for an element in the new document, and wherein the
page flow model generates the scanning script to describe new
coordinate locations of the new zone and the another zone in the
new document; and using the scanning script to scan the new
document to generate digital text.
14. The computer system of claim 13, to perform further operations
comprising: generating a plurality of page flow models, wherein
each of the page flow models is associated with a different
document type, and wherein another document type of another
document is used to select a corresponding page flow model of the
plurality of page flow models.
15. The computer system of claim 13, wherein the page flow model
further comprises: extracting text values for the text area in the
new document; determining that the text values are accurate; and
producing intermediate output that includes the new coordinate
locations of the new zone for the text area, a label for the text
area, and text the text values for the text area.
16. The computer system of claim 15, wherein the page flow model
further comprises: calculating a percentage of inaccuracy of the
new zone and the another zone; and for the new zone and the another
zone, adjusting a size of that zone until the percentage of
inaccuracy for that zone is less than an inaccuracy threshold.
17. The computer system of claim 15, wherein the scanning script
comprises, for the new zone, the adjusted coordinate locations, the
label, the text values, and an extraction confidence.
18. The computer system of claim 13, wherein a Software as a
Service (SaaS) is configured to perform the operations of the
computer system.
Description
BACKGROUND
[0001] Embodiments of the invention relate to Optical Character
Recognition (OCR) induction for multi-page changes.
[0002] Typed, handwritten or printed text may be scanned to form
images. A set of images may represent pages of a document. The
pages have elements, which include text areas (e.g., one for a name
field and another for an address field), labels for the text areas
(e.g., "Name" and "Address"), and other page content. OCR may be
described as conversion of these images into digital text.
[0003] Some text areas may be variable length. For OCR processing
of documents that have variable length text areas, the amount of
text input into the text areas may change the page (e.g., a text
field is shifted on the page) or the pagination of the document
(e.g., where elements move from page number 1, to page number
two).
[0004] An OCR processor may be trained to recognize the text areas.
In particular, the OCR processor may be trained on where text areas
are in a page (e.g., coordinate location (x1, y1) is a starting
position for a name, coordinate location (x50, y70) is a starting
position for an address, etc.). There are some instances when there
is a variable length text area that allows a user to enter text
into that text area and that causes a shift of another text area
onto a next page, and this may result in the pages of the document
being out of sequence. In conventional systems, since the OCR model
is not trained for such overflow and having the pages out of
sequence, the OCR processing may result in errors. For example, in
a document, page 1 is expected to have demographics, page 2 is
expected to have activity history, page 3 is expected to have
awards. However, if the text for page 1 extends into page 2, then
page 2 has both demographics and activity history and may even
overflow to page 3, thus changing both pages 2 and 3. With this, it
is not just pages getting out of sequence, but also each page
looking different with elements in the pages shifting.
SUMMARY
[0005] In accordance with certain embodiments, a
computer-implemented method is provided for OCR induction for
multi-page changes. The computer-implemented method comprises
operations. A plurality of documents of a document type are
processed to generate text area data for a text area in one or more
documents of the plurality of documents, where the text area data
includes coordinate locations of a zone for the text area based on
expansion and direction of shift of the text area, and where the
expansion is based on a font type and a font size of text values in
the text area. A page flow model is trained using the plurality of
documents and the text area data. In response to receiving a new
document comprising the text area, a scanning script is received
from the page flow model, where the page flow model identifies a
new zone for the text area in the new document and determines how
to adjust another zone for an element in the new document, and
where the page flow model generates the scanning script to describe
new coordinate locations of the new zone and the another zone in
the new document. The scanning script is used to scan the new
document to generate digital text.
[0006] In accordance with other embodiments, a computer program
product is provided for OCR induction for multi-page changes. The
computer program product comprises a computer readable storage
medium having program code embodied therewith, the program code
executable by at least one processor to perform operations. A
plurality of documents of a document type are processed to generate
text area data for a text area in one or more documents of the
plurality of documents, where the text area data includes
coordinate locations of a zone for the text area based on expansion
and direction of shift of the text area, and where the expansion is
based on a font type and a font size of text values in the text
area. A page flow model is trained using the plurality of documents
and the text area data. In response to receiving a new document
comprising the text area, a scanning script is received from the
page flow model, where the page flow model identifies a new zone
for the text area in the new document and determines how to adjust
another zone for an element in the new document, and where the page
flow model generates the scanning script to describe new coordinate
locations of the new zone and the another zone in the new document.
The scanning script is used to scan the new document to generate
digital text.
[0007] In accordance with yet other embodiments, a computer system
is provided for OCR induction for multi-page changes. The computer
system comprises one or more processors, one or more
computer-readable memories and one or more computer-readable,
tangible storage devices; and program instructions, stored on at
least one of the one or more computer-readable, tangible storage
devices for execution by at least one of the one or more processors
via at least one of the one or more memories, to perform
operations. A plurality of documents of a document type are
processed to generate text area data for a text area in one or more
documents of the plurality of documents, where the text area data
includes coordinate locations of a zone for the text area based on
expansion and direction of shift of the text area, and where the
expansion is based on a font type and a font size of text values in
the text area. A page flow model is trained using the plurality of
documents and the text area data. In response to receiving a new
document comprising the text area, a scanning script is received
from the page flow model, where the page flow model identifies a
new zone for the text area in the new document and determines how
to adjust another zone for an element in the new document, and
where the page flow model generates the scanning script to describe
new coordinate locations of the new zone and the another zone in
the new document. The scanning script is used to scan the new
document to generate digital text.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0008] Referring now to the drawings in which like reference
numbers represent corresponding parts throughout:
[0009] FIG. 1 illustrates, in a block diagram, a computing
environment in accordance with certain embodiments.
[0010] FIG. 2 illustrates example pages in accordance with certain
embodiments.
[0011] FIGS. 3A and 3B illustrate, in a flowchart, operations for
creating a page flow model in accordance with certain
embodiments.
[0012] FIGS. 4A and 4B illustrate, in a flowchart, operations
performed by the page flow model to generate a scanning script in
accordance with certain embodiments.
[0013] FIG. 5 illustrates, in a flowchart, operations for using the
scanning script to process a document in accordance with certain
embodiments.
[0014] FIG. 6 illustrates examples of scanning scripts in
accordance with certain embodiments.
[0015] FIG. 7 illustrates, in a block diagram, details of a machine
learning module in accordance with certain embodiments.
[0016] FIG. 8 illustrates a computing node in accordance with
certain embodiments.
[0017] FIG. 9 illustrates a cloud computing environment in
accordance with certain embodiments.
[0018] FIG. 10 illustrates abstraction model layers in accordance
with certain embodiments.
DETAILED DESCRIPTION
[0019] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
[0020] Embodiments are able to predict when text areas on a page
will overflow (e.g., result in moving an element to another page)
or underflow (e.g., result in moving an element on a same page). An
element may be a text area, a label, and other page content (e.g.,
an image, additional text not associated with a text area, etc.).
Then, embodiments train a page flow model based on the possible
overflow or underflow and change the location information (e.g.,
coordinate locations) of zones for the text areas to enable better
processing of the text values in the text areas. Text areas may be
described as being within zones.
[0021] FIG. 1 illustrates, in a block diagram, a computing
environment in accordance with certain embodiments. A computing
device 100 is connected to a data store 150. The computing device
100 includes an OCR engine 110, which includes an text flow engine
120. The computing device 100 includes text area data 130 for text
areas, page flow models 132 (learning templates), intermediate
output 134, and scanning scripts 136.
[0022] In certain embodiments, there is text area data for each
document type (e.g., if three document types have been processed,
then there are three text area data files).
[0023] The data store 150 includes documents 160, and the documents
160 have document types (e.g., a user profile document type, a
medical billing document type, etc.). The documents 160 have pages
with text areas. In certain embodiments, one or more of the pages
of a document may not have text areas.
[0024] The text flow engine 120 processes the documents 160 to
generate page flow models 132. In certain embodiments, each page
flow model 132 is associated with a particular document type (e.g.,
one page flow model 132 is for a user profile document type, while
another page flow model 132 is for a medical billing document
type). In certain embodiments, the page flow models 132 are machine
learning models, such as regression models or neural network
models. The page flow models 132 generate the intermediate output
134 and the scanning scripts 136.
[0025] The text flow engine 120 calculates the potential sizes of
text areas that have variable length text areas on each page of a
document 160 based on the pixel size of text values in the text
areas, taking into consideration font (type and size) and size
(length) of text values (i.e., number of characters) and,
optionally, historical data. A text area may be described as a text
box or an input area that stores text values. In certain
embodiments, the text areas have a label (name), a position
(coordinate location x, y), a size, and, optionally, a font type
and font size. The text values stored in a text area may be
alphabetic characters, numbers, symbols, links to other documents,
images or links to images, etc.
[0026] The text areas may be described as being within bounding
boxes. The text flow engine 120 extrapolates the coordinate
locations of the zone (e.g., a bounding box) for each text area of
each page and deduces possible page overflow or underflow. A
bounding box represents the entire area that the text area may
expand to. In certain embodiments, the coordinate locations of the
zone for the text area may be four coordinate locations
corresponding to the corners of the zone.
[0027] Based on the possible page overflows or underflows, the text
flow engine 120 generates a page flow model 132, and the page flow
model 132 generates a scanning script 136 that depicts the overflow
or underflow of elements on the page and enables post processing
correction of elements based on the text areas and the shifts of
elements on each page.
[0028] Initially, the text flow engine 120 first looks for these
variable length text areas in documents and dynamically selects or
approximates complementary page flow models 132 for processing a
document of a particular document type and uses the page flow model
132 to process the elements of pages of a document and generate a
scanning script 136.
[0029] FIG. 2 illustrates example documents 210, 220 in accordance
with certain embodiments. In this example, the documents 210, 220
each have one page. For documents 210, 220, the name ("User A",
"User B") is zone-based. With zone-based
[0030] OCR, the OCR engine 110 is trained to recognize a specific
part of a document. With document 210, the answer to the question
"What is your objective for joining the program?" is short and does
not push down the question "When can you start?". On the other
hand, with document 220, the answer to the question "What is your
objective for joining the program?" is long, which expands this
field and pushes the question "When can you start?" farther down
the page. In this example, the start date is not accessible with
conventional processing because the start date field has been
pushed downward. However, the OCR engine 110 and the text flow
engine 120 are able to process the start date field that has
shifted downwards.
[0031] FIGS. 3A and 3B illustrate, in a flowchart, operations for
creating a page flow model 132 in accordance with certain
embodiments. Control begins at block 300 with the text flow engine
120 receiving multiple documents having a same document type.
Different documents having the same document type have the same one
or more text boxes, but the size of the text boxes may change based
on the text values (e.g., based on the number of characters, font
type, font size) in those text boxes. In certain embodiments, a
document has one or more text boxes, but a particular page of the
document may have zero or more text boxes.
[0032] In block 302, the text flow engine 120 selects a next
document of the multiple documents, starting with a first document.
In block 304, the text flow engine 120 selects a next page of the
document that has a text area, starting with a first page having a
text area. With embodiments, pages without text areas may be
skipped. For example, the document has: page 1 with no text area
and page 2 with a text area, then page 2 is selected, while page 1
is skipped. In block 306, the text flow engine select a next text
area of the page of the document, starting with a first text area.
In certain embodiments, the text area selected is a variable length
text area.
[0033] In block 308, the text flow engine 120 predicts how far the
text area may expand based on a font type and a font size of text
values in the text area (i.e., predicts the bounding box for the
text box). Predicting how far the text area may expand indicates
that at a particular font type and font size, the text area will
take X space. In certain embodiments, the font type and the font
size are determined from the data in that text area. In certain
embodiments, a font type and a font size are associated with the
text box. In certain embodiments, different documents of the same
type may use different font types and different font sizes for the
same text field.
[0034] In certain embodiments, the text flow engine 120 obtains an
upper bound for the text area size from the page itself (e.g.,
based on the text area accepting 1000 characters) or from
historical data for that text area and for that document type
(e.g., a user profile document type, a medical billing document
type, etc.). The text flow engine 120 obtains the font type and
font size for the text area from the page and extrapolates that to
a pixel size of the text. The text flow engine 120 evaluates a
maximum variable length text area using medium to large character
types based on the maximum characters allowed for that text area.
Then, the text flow engine 120 determines the vertical and/or
horizontal coordinate shifts of a zone for the text area based on
the overall coordinate locations of the page for the maximum
variable length.
[0035] From block 308 (FIG. 3A), processing continues to block 310
(FIG. 3B). In block 310, the text flow engine 120 determines one or
more directions that the text area may shift. In certain
embodiments, the text flow engine 120 obtains the one or more
directions from a source Hypertext Markup Language (HTML). For
example, Cascading Style Sheets (CSS) describe how HTML elements
are to be displayed on a page and indicates the way the HTML
elements may flow. In certain embodiments, the orientation of the
shift may be shifted vertically (down or up) and/or shifted
horizontally (left or right).
[0036] In block 312, the text flow engine 120 stores text area data
130 for the text area that includes the coordinate locations of a
zone for the text area, a label for the text area, and text values
for the text area. The coordinate locations of the zone are based
on the expansion and the direction of the shift. The coordinate
locations may be provided using one or more (longitude x, latitude
y) coordinates.
[0037] In block 314, the text flow engine 120 determines whether
there are is another text area to select on this page. If so,
processing continues to block 306 (FIG. 3A) to select the next text
area, otherwise, processing continues to block 316.
[0038] In block 316, the text flow engine 120 determines whether
there is another page to select for this document. If so,
processing continues to block 304 (FIG. 3A), otherwise, processing
continues to block 318.
[0039] In block 318, the text flow engine 120 determines whether
there is another document to select from the multiple documents. If
so, processing continues to block 302 (FIG. 3A), otherwise,
processing continues to block 320.
[0040] In block 320, the text flow engine 120 generates and trains
a page flow model 132 for the document type based on the expansion
and direction of shift of the text areas. The page flow model 132
is stored. Then, when a document of the document type is received,
the page flow model 132 for that document type is used to generate
a scanning script for the document. For example, FIG. 2 illustrates
two documents 210, 220 having the same document type, "General
Intake Form", but with different information in the text areas.
These documents 210, 220, along with other documents of the same
document type, may be used to train the page flow model 132.
[0041] Thus, the text flow engine 120 trains the page flow model
132 based on different combinations of expansion and direction of
shift of text areas found in the different documents. The page flow
model 132 adjusts and moves additional elements (e.g., labels of
other text areas and the other text areas) on one or more pages of
a document to shift their coordinate locations. The page flow model
132 provides a scanning script that indicates the new coordinate
locations of the zones for the text areas and other elements.
[0042] The page flow model 132 is trained by the text flow engine
120 to handle adjustments for middle of page shifts that move
related text areas either with movement of an existing text area or
without movement of that existing text area. The page flow model
132 is trained to utilize zone-based adjustments for the page.
Zones may be described as where the text area is expected to be
located after the expansion and shift. The page flow model 132 may
add the zone-based adjustments to the text area data 130 for post
processing each page.
[0043] With training of the page flow model 132 allows for creation
of a scanning script 136 that describes where an element resides
and the flow of the elements and their coordinate locations on the
pages of the document to enable more accurate processing of the
elements of that document when performing OCR processing to
digitize the document.
[0044] With embodiments, the processing of FIGS. 3A and 3B occurs
for each document type to generate a train a page flow model 132
for that document type.
[0045] FIGS. 4A and 4B illustrate, in a flowchart, operations
performed by the page flow model 132 to generate a scanning script
in accordance with certain embodiments. Control begins at block 400
with the page flow model 132 receiving a document having a document
type and text area data 130 for that document type.
[0046] In block 402, the page flow model 132 determines coordinate
locations of the zones (e.g., the best fit variable zones) for text
areas in the document. The page flow model 132 evaluates location
and size of variable zones based on predicted zone information
(e.g., using coordinate locations stored in the text area data
130). In certain embodiments, this determination of the best fit
variable zones includes processing the document to identify
elements, including the text areas, and values in the text
areas.
[0047] In block 404, the page flow model 132 determines that the
text values extracted from the text areas are accurate. That is,
the page flow model 132 performs a Natural Language Processing
(NLP) completeness check for the domain based on extracted text
values (e.g., if the date is scanned, the text area size does not
change). In certain embodiments, the domain is based on the content
within the box and the text area label. For example, if the textbox
is "starting date", then the page flow model 132 tries to
transcribe the content of the text area into a valid date. In other
embodiments, the document type may be used for determining domain
in addition to using the label and the content of the text area. In
certain embodiments, if the page flow model 132 finds that the
extracted text values are not accurate, further processing is done
(e.g., the text values are extracted again, an error message is
sent to a system administrator, etc.).
[0048] In block 406, the page flow model 132 produces intermediate
output 134 that includes coordinate locations of the zones for the
text areas, labels for the text areas, and text values for the text
areas.
[0049] In block 408, the page flow model 132 determines a
percentage of in accuracy of each of the zones. In certain
embodiments, the percentage of accuracy may also be referred to as
an extraction confidence. In certain embodiments, for each of the
zones, the page flow model 132 takes text from a variable length
text area within that zone and calculates an expected size of the
zone. The page flow model 132 matches the expected size of the zone
with a historical size of the zone (e.g., a size of the zone based
on "known-good" OCR output) to deduce the percentage of inaccuracy
of the zone. The historical size of the zone may be based on
historical OCR output that has been stored from previous OCR
processing.
[0050] In block 410, the page flow model 132 ranks the zones by
percentage of inaccuracy in a list. The zones are ranked from the
highest percentage of inaccuracy to the lowest percentage of
inaccuracy. In certain embodiments, there is one zone for one text
area that causes other elements (e.g., text or images) to
shift.
[0051] In block 412, the page flow model 132 selects a next zone on
the list, starting with a zone having the highest percentage of
inaccuracy. From block 412 (FIG. 4A), processing continues to block
414 (FIG. 4B).
[0052] In block 414, the page flow model 132 determines whether the
percentage of inaccuracy for the zone exceeds (is greater than) an
inaccuracy threshold. If so, processing continues to block 416,
otherwise, processing continues to block 418. In certain
embodiments, the page flow model 132 adjusts accuracy for the zone
for a text area and other zones in the document if the expected
size of the zone is determined to be over a pixel distance
threshold from the historical size of the zone. In certain
embodiments, the page flow model 132 estimates the length of text
values in a text area from historical data and multiplies that by
the font size to get a pixel size for the expected size of the
zone, and this pixel size is compared to a pixel distance
threshold.
[0053] The page flow model 132 adjusts other elements in the
document, as needed. In particular, embodiments perform multiple
adjustments. For example, if the zone is changed for one box, then
the zone may need to be adjusted for other boxes that occur before
or after that box in the same document.
[0054] In block 416, the page flow model 132 adjusts the size of
the zone (i.e., adjusts the coordinate locations of the zone) and,
optionally, other elements (e.g., other zones and labels) impacted
by adjusting the size of the zone. From block 416 (FIG. 4B),
processing continues to block 406 (FIG. 4A).
[0055] For example, if zone 2 is a long text field and zone 3 is a
date, then, if zone 3 starts too high, zone 3 would get text and a
date (which is not very accurate). In this example, zone 2 is
scored as accurate since it contains text. However, if zone 3 is
adjusted to start lower, and zones 2 and 3 are vertically adjacent
zones (i.e., zone 2 is above zone 3), then zone 2 is adjusted to
end lower.
[0056] In block 418, the page flow model 132 determines whether
there is another zone to select on the list. If so, processing
continues to block 412 (FIG. 4A), otherwise, processing continues
to block 420.
[0057] In block 420, the page flow model 132 generates a scanning
script from the intermediate output 134. In block 422, the page
flow model 132 returns the scanning script 136 (i.e., final output
information) for OCR processing of a document with multiple pages.
The scanning script 136 may be described as providing the OCR
engine 110 with information on better processing of a document with
variable length text areas.
[0058] Thus, the zones may be adjusted based on the expected size
for zones to provide more accurate information in the scanning
script 136 for the OCR engine 110.
[0059] The adjustments to each zone may be to the zone vertically
and/or horizontally. For example, if the text area is expected to
overflow, then the OCR engine 110 is notified, via the scanning
script, to scan a different and/or bigger area for that text area.
Thus, the adjustment is performed to provide more accurate
information for scanning.
[0060] The adjustments from blocks 406-418 allow for post
processing correction of text values, zones, and other elements
based on the shifts of elements. Thus, embodiments correct scanned
text.
[0061] FIG. 5 illustrates, in a flowchart, operations for using the
scanning script to process a document in accordance with certain
embodiments. Control begins at block 500 with the text flow engine
120 receiving a document with multiple pages, where the document
has a document type. In block 502, the text flow engine 120
identifies (i.e., selects) a page flow model 132 from a plurality
of page flow models, where the page flow model 132 was created for
the document type. In certain embodiments, each of the page flow
models 132 is associated with an accuracy based on how accurate the
scan of a document was using that page flow model 132 (e.g., from
historical data), and the text flow engine 120 selects the most
accurate model (i.e., the one that scanned most or all of the text
areas to the correct field types). In certain embodiments, each
page flow model 132 is associated with a font and a font size.
[0062] In block 504, the text flow engine 120 uses the page flow
model 132 to process the document and generate a scanning script,
where the page flow model 132 improves accuracy by making
adjustments to the scanning script that take into account overflows
and underflows of variable length text areas.
[0063] In block 506, the text flow engine 120 (or OCR engine 110)
uses the scanning script 136 to scan the document and generate
digital text. Because a scanning script 136 is selected, the text
flow engine 120 is able to process a document for which text areas
have caused elements to shift on a page or onto another page. In
this manner, embodiments provide OCR induction for multi-page
changes.
[0064] FIG. 6 illustrates examples of scanning scripts 610, 620 in
accordance with certain embodiments. The OCR engine 110 generates
scanning scripts 610, 620 using a page flow model 132 created by
the text flow engine 120 for documents 210, 220. In certain
embodiments, the OCR engine 110 produces structured scanning
scripts 610, 620, for example, as a JavaScript.RTM. Object Notation
(JSON) document. (JavaScript is a registered trademark of Oracle
Corporation in the United States and/or other countries.)
[0065] In this example, the scanning scripts 610, 620 include the
field names and text values, along with additional metadata. The
additional metadata includes coordinate locations of the zones for
the text areas and an accuracy ("extraction_confidence"). In
certain embodiments, accuracy may be based on whether the content
matches the domain and may be assessed by a probabilistic model
scored 0-1.
[0066] With embodiments, the text flow engine 120 considers
location on each page of a document and context of headers to
provide better error correction of the document that has been
processed by the OCR engine 110. Also, the text flow engine 120
improves scanning of sub-sections (e.g., text areas) of each page
of the document.
[0067] With embodiments, the text flow engine 120 extrapolates the
potential sub-sections based on the variability of text areas
without actual data and improves the accuracy for processing
documents with the variable length text areas.
[0068] With embodiments, the text flow engine 120 predicts expected
skew based on the document contents and performs an analysis of the
scanned text of the document to determine whether a corrective scan
is to be performed.
[0069] In certain embodiments, the text flow engine 120 predicts a
page overflow or underflow for training a page overflow model based
on possible overflow or underflow of elements that may end on
different pages for changing the location of such page information
within the results of the OCR engine 110.
[0070] In certain embodiments, the text flow engine 120 calculates
a plurality of sizes of a text area that is variable length based
on a pixel size of text values in the text area, where the pixel
sizes is based on the font type and the font size of the text
values. The text flow engine 120 extrapolates coordinate locations
for a bounding box of values of a text area for a first page of a
document comprising a plurality of pages. The text flow engine 120
deduces a page overflow or underflow based on the coordinate
locations for the bounding box to generate a page flow model 132,
where the page flow model 132 depicts overflow or underflow
elements that shift. The text flow engine 120 receives a new
document comprising a plurality of pages for processing by the OCR
engine 110 and selects a page flow model 132 of multiple page flow
models (with different page flow models for different document
types). Then, the text flow engine 120 processes the new document
comprising the plurality of pages using a scanning script produced
by the page flow model 132.
[0071] Embodiments are applicable to both overflow and underflow.
For example, with overflow elements on page 1 shift into page 2,
which may cause some elements from page 2 to shift to page 3. With
underflow, less text than expected is in the text area, and
elements on page 3 shift to page 2.
[0072] Embodiments handle variability of variable length text areas
for modeling (i.e., training the page flow models 132), processing
execution (i.e., to determine coordinate locations of the zones and
other elements), and post processing of the zones and other
elements (prior to generating the scanning script).
[0073] Embodiments calculate the potential sizes of text areas
based on the pixel size of values in those text areas, taking in
consideration font type and font size of the values, then
extrapolate the (x, y) coordinates for the bounding boxes of these
values, and deduce page overflows or underflows.
[0074] FIG. 7 illustrates, in a block diagram, details of a machine
learning module 700 in accordance with certain embodiments. In
certain embodiments, the one or more models 132 are implemented
using the components of the machine learning module 700.
[0075] The machine learning module 700 may comprise a neural
network with a collection of nodes with links connecting them,
where the links are referred to as connections. For example, FIG. 7
shows a node 704 connected by a connection 708 to the node 706. The
collection of nodes may be organized into three main parts: an
input layer 710, one or more hidden layers 712, and an output layer
714.
[0076] The connection between one node and another is represented
by a number called a weight, where the weight may be either
positive (if one node excites another) or negative (if one node
suppresses or inhibits another). Training the machine learning
module 700 entails calibrating the weights in the machine learning
module 700 via mechanisms referred to as forward propagation 716
and backward propagation 722. Bias nodes that are not connected to
any previous layer may also be maintained in the machine learning
module 700. A bias may be described as an extra input of 1 with a
weight attached to it fora node.
[0077] In forward propagation 716, a set of weights are applied to
the input data 718 . . . 320 to calculate the output 724. For the
first forward propagation, the set of weights may be selected
randomly or set by, for example, a system administrator. That is,
in the forward propagation 716, embodiments apply a set of weights
to the input data 718 . . . 720 and calculate an output 724.
[0078] In backward propagation 722 a measurement is made for a
margin of error of the output 724, and the weights are adjusted to
decrease the error. Backward propagation 722 compares the output
that the machine learning module 700 produces with the output that
the machine learning module 700 was meant to produce, and uses the
difference between them to modify the weights of the connections
between the nodes of the machine learning module 700, starting from
the output layer 714 through the hidden layers 712 to the input
layer 710, i.e., going backward in the machine learning module 700.
In time, backward propagation 722 causes the machine learning
module 700 to learn, reducing the difference between actual and
intended output to the point where the two come very close or
coincide.
[0079] The machine learning module 700 may be trained using
backward propagation to adjust weights at nodes in a hidden layer
to produce adjusted output values based on the provided inputs 718
. . . 720. A margin of error may be determined with respect to the
actual output 724 from the machine learning module 600 and an
expected output to train the machine learning module 700 to produce
the desired output value based on a calculated expected output. In
backward propagation, the margin of error of the output may be
measured and the weights at nodes in the hidden layers 712 may be
adjusted accordingly to decrease the error.
[0080] Backward propagation may comprise a technique for supervised
learning of artificial neural networks using gradient descent.
Given an artificial neural network and an error function, the
technique may calculate the gradient of the error function with
respect to the artificial neural network's weights.
[0081] Thus, the machine learning module 700 is configured to
repeat both forward and backward propagation until the weights of
the machine learning module 700 are calibrated to accurately
predict an output.
[0082] The machine learning module 700 implements a machine
learning technique such as decision tree learning, association rule
learning, artificial neural network, inductive programming logic,
support vector machines, Bayesian models, etc., to determine the
output value 724.
[0083] In certain machine learning module 700 implementations,
weights in a hidden layer of nodes may be assigned to these inputs
to indicate their predictive quality in relation to other of the
inputs based on training to reach the output value 724.
[0084] With embodiments, the machine learning module 700 is a
neural network, which may be described as a collection of "neurons"
with "synapses" connecting them.
[0085] With embodiments, there may be multiple hidden layers 712,
with the term "deep" learning implying multiple hidden layers.
Hidden layers 712 may be useful when the neural network has to make
sense of something complicated, contextual, or non-obvious, such as
image recognition. The term "deep" learning comes from having many
hidden layers. These layers are known as "hidden", since they are
not visible as a network output.
[0086] In certain embodiments, training a neural network may be
described as calibrating all of the "weights" by repeating the
forward propagation 716 and the backward propagation 722.
[0087] In backward propagation 722, embodiments measure the margin
of error of the output and adjust the weights accordingly to
decrease the error.
[0088] Neural networks repeat both forward and backward propagation
until the weights are calibrated to accurately predict the output
724.
[0089] In certain embodiments, the inputs to the machine learning
module 700 are documents 160 with one or more text areas in one or
more pages (e.g., raw form documents) and text area data 130 for
those documents 160, and the outputs of the machine learning module
700 is a scanning script 136. In certain embodiments, the machine
learning model may be refined based on whether the outputted
recommendations, once taken, generate positive outcomes.
[0090] FIG. 8 illustrates a computing environment 810 in accordance
with certain embodiments. In certain embodiments, the computing
environment is a cloud computing environment. Referring to FIG. 8,
computer node 812 is only one example of a suitable computing node
and is not intended to suggest any limitation as to the scope of
use or functionality of embodiments of the invention described
herein. Regardless, computer node 812 is capable of being
implemented and/or performing any of the functionality set forth
hereinabove.
[0091] The computer node 812 may be a computer system, which is
operational with numerous other general purpose or special purpose
computing system environments or configurations. Examples of
well-known computing systems, environments, and/or configurations
that may be suitable for use with computer node 812 include, but
are not limited to, personal computer systems, server computer
systems, thin clients, thick clients, handheld or laptop devices,
multiprocessor systems, microprocessor-based systems, set top
boxes, programmable consumer electronics, network PCs, minicomputer
systems, mainframe computer systems, and distributed cloud
computing environments that include any of the above systems or
devices, and the like.
[0092] Computer node 812 may be described in the general context of
computer system executable instructions, such as program modules,
being executed by a computer system. Generally, program modules may
include routines, programs, objects, components, logic, data
structures, and so on that perform particular tasks or implement
particular abstract data types. Computer node 812 may be practiced
in distributed cloud computing environments where tasks are
performed by remote processing devices that are linked through a
communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
[0093] As shown in FIG. 8, computer node 812 is shown in the form
of a general-purpose computing device. The components of computer
node 812 may include, but are not limited to, one or more
processors or processing units 816, a system memory 828, and a bus
818 that couples various system components including system memory
828 to one or more processors or processing units 816.
[0094] Bus 818 represents one or more of any of several types of
bus structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
[0095] Computer node 812 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by computer node 812, and it includes both volatile
and non-volatile media, removable and non-removable media.
[0096] System memory 828 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
830 and/or cache memory 832. Computer node 812 may further include
other removable/non-removable, volatile/non-volatile computer
system storage media. By way of example only, storage system 834
can be provided for reading from and writing to a non-removable,
non-volatile magnetic media (not shown and typically called a "hard
drive"). Although not shown, a magnetic disk drive for reading from
and writing to a removable, non-volatile magnetic disk (e.g., a
"floppy disk"), and an optical disk drive for reading from or
writing to a removable, non-volatile optical disk such as a CD-ROM,
DVD-ROM or other optical media can be provided. In such instances,
each can be connected to bus 818 by one or more data media
interfaces. As will be further depicted and described below, system
memory 828 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
[0097] Program/utility 840, having a set (at least one) of program
modules 842, may be stored in system memory 828 by way of example,
and not limitation, as well as an operating system, one or more
application programs, other program modules, and program data. Each
of the operating system, one or more application programs, other
program modules, and program data or some combination thereof, may
include an implementation of a networking environment. Program
modules 842 generally carry out the functions and/or methodologies
of embodiments of the invention as described herein.
[0098] Computer node 812 may also communicate with one or more
external devices 814 such as a keyboard, a pointing device, a
display 824, etc.; one or more devices that enable a user to
interact with computer node 812; and/or any devices (e.g., network
card, modem, etc.) that enable computer node 812 to communicate
with one or more other computing devices. Such communication can
occur via Input/Output (I/O) interfaces 822. Still yet, computer
node 812 can communicate with one or more networks such as a local
area network (LAN), a general wide area network (WAN), and/or a
public network (e.g., the Internet) via network adapter 820. As
depicted, network adapter 820 communicates with the other
components of computer node 812 via bus 818. It should be
understood that although not shown, other hardware and/or software
components could be used in conjunction with computer node 812.
Examples, include, but are not limited to: microcode, device
drivers, redundant processing units, external disk drive arrays,
RAID systems, tape drives, and data archival storage systems,
etc.
[0099] In certain embodiments, the computing device 100 has the
architecture of computer node 812. In certain embodiments, the
computing device 100 is part of a cloud infrastructure. In certain
alternative embodiments, the computing device 100 is not part of a
cloud infrastructure.
Cloud Embodiments
[0100] It is to be understood that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0101] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0102] Characteristics are as follows:
[0103] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0104] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0105] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0106] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0107] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts).
[0108] Resource usage can be monitored, controlled, and reported,
providing transparency for both the provider and consumer of the
utilized service.
[0109] Service Models are as follows:
[0110] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0111] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0112] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0113] Deployment Models are as follows:
[0114] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0115] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0116] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0117] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0118] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
[0119] Referring now to FIG. 9, illustrative cloud computing
environment 950 is depicted. As shown, cloud computing environment
950 includes one or more cloud computing nodes 910 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 954A,
desktop computer 954B, laptop computer 954C, and/or automobile
computer system 954N may communicate. Nodes 910 may communicate
with one another. They may be grouped (not shown) physically or
virtually, in one or more networks, such as Private, Community,
Public, or Hybrid clouds as described hereinabove, or a combination
thereof. This allows cloud computing environment 950 to offer
infrastructure, platforms and/or software as services for which a
cloud consumer does not need to maintain resources on a local
computing device. It is understood that the types of computing
devices 954A-N shown in FIG. 9 are intended to be illustrative only
and that computing nodes 910 and cloud computing environment 950
can communicate with any type of computerized device over any type
of network and/or network addressable connection (e.g., using a web
browser).
[0120] Referring now to FIG. 10, a set of functional abstraction
layers provided by cloud computing environment 950 (FIG. 9) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 10 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0121] Hardware and software layer 1060 includes hardware and
software components. Examples of hardware components include:
mainframes 1061; RISC (Reduced Instruction Set Computer)
architecture based servers 1062; servers 1063; blade servers 1064;
storage devices 1065; and networks and networking components 1066.
In some embodiments, software components include network
application server software 1067 and database software 1068.
[0122] Virtualization layer 1070 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 1071; virtual storage 1072; virtual networks 1073,
including virtual private networks; virtual applications and
operating systems 1074; and virtual clients 1075.
[0123] In one example, management layer 1080 may provide the
functions described below. Resource provisioning 1081 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 1082 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may include application software licenses.
Security provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 1083 provides access to the cloud computing environment for
consumers and system administrators. Service level management 1084
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 1085 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0124] Workloads layer 1090 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 1091; software development and
lifecycle management 1092; virtual classroom education delivery
1093; data analytics processing 1094; transaction processing 1095;
and OCR induction for multi-page changes 1096.
[0125] Thus, in certain embodiments, software or a program,
implementing OCR induction for multi-page changes in accordance
with embodiments described herein, is provided as a service in a
cloud environment.
Additional Embodiment Details
[0126] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0127] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0128] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0129] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
[0130] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0131] These computer readable program instructions may be provided
to a processor of a computer, or other programmable data processing
apparatus to produce a machine, such that the instructions, which
execute via the processor of the computer or other programmable
data processing apparatus, create means for implementing the
functions/acts specified in the flowchart and/or block diagram
block or blocks. These computer readable program instructions may
also be stored in a computer readable storage medium that can
direct a computer, a programmable data processing apparatus, and/or
other devices to function in a particular manner, such that the
computer readable storage medium having instructions stored therein
comprises an article of manufacture including instructions which
implement aspects of the function/act specified in the flowchart
and/or block diagram block or blocks.
[0132] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0133] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be accomplished as one step, executed concurrently,
substantially concurrently, in a partially or wholly temporally
overlapping manner, or the blocks may sometimes be executed in the
reverse order, depending upon the functionality involved. It will
also be noted that each block of the block diagrams and/or
flowchart illustration, and combinations of blocks in the block
diagrams and/or flowchart illustration, can be implemented by
special purpose hardware-based systems that perform the specified
functions or acts or carry out combinations of special purpose
hardware and computer instructions.
[0134] The terms "an embodiment", "embodiment", "embodiments", "the
embodiment", "the embodiments", "one or more embodiments", "some
embodiments", and "one embodiment" mean "one or more (but not all)
embodiments of the present invention(s)" unless expressly specified
otherwise.
[0135] The terms "including", "comprising", "having" and variations
thereof mean "including but not limited to", unless expressly
specified otherwise.
[0136] The enumerated listing of items does not imply that any or
all of the items are mutually exclusive, unless expressly specified
otherwise.
[0137] The terms "a", "an" and "the" mean "one or more", unless
expressly specified otherwise.
[0138] Devices that are in communication with each other need not
be in continuous communication with each other, unless expressly
specified otherwise. In addition, devices that are in communication
with each other may communicate directly or indirectly through one
or more intermediaries.
[0139] A description of an embodiment with several components in
communication with each other does not imply that all such
components are required. On the contrary a variety of optional
components are described to illustrate the wide variety of possible
embodiments of the present invention.
[0140] When a single device or article is described herein, it will
be readily apparent that more than one device/article (whether or
not they cooperate) may be used in place of a single
device/article. Similarly, where more than one device or article is
described herein (whether or not they cooperate), it will be
readily apparent that a single device/article may be used in place
of the more than one device or article or a different number of
devices/articles may be used instead of the shown number of devices
or programs. The functionality and/or the features of a device may
be alternatively embodied by one or more other devices which are
not explicitly described as having such functionality/features.
Thus, other embodiments of the present invention need not include
the device itself.
[0141] The foregoing description of various embodiments of the
invention has been presented for the purposes of illustration and
description. It is not intended to be exhaustive or to limit the
invention to the precise form disclosed. Many modifications and
variations are possible in light of the above teaching. It is
intended that the scope of the invention be limited not by this
detailed description, but rather by the claims appended hereto. The
above specification, examples and data provide a complete
description of the manufacture and use of the composition of the
invention. Since many embodiments of the invention can be made
without departing from the spirit and scope of the invention,
embodiments of the invention reside in the claims herein after
appended. The foregoing description provides examples of
embodiments of the invention, and variations and substitutions may
be made in other embodiments.
* * * * *